Imaging systems, such as scanning laser ophthalmoscopes (SLOs), are known to capture retinal image data using one or more digital image sensors. Digital image sensors for SLOs are commonly a single sensor in which the light intensity signal is synchronised with the scanning position signal in order to produce a single stream of data that can be synchronised into a 2D image. Digital image sensors may alternatively include an array of light sensitive picture elements (pixels) Retinal images produced by SLOs or other retinal imaging apparatuses such as fundus cameras are typically two dimensional pixel arrays and are termed digital retinal images.
The set of intensity values derived from the pixel array is known as image data. The “raw” image data output by the pixel array may be subjected to various post-processing techniques in order to reproduce an image either for viewing by a human or for processing by a machine. Post-processing techniques of retinal images include various statistical methods for image analysis and registration of pairs or sequences of retinal images.
Registering pairs or sequences of retinal images generally concerns the scaling, rotation and translation of one or more images with respect to a base image in order to align (“register”) the image with the base image. The registered retinal images are typically superimposed with the base retinal image to facilitate comparisons between the images.
Algorithms which enable affine registration of pairs or sequences of retinal images are known. Such algorithms may involve “vasculature tracking”, which involves iterative searches and decision trees to map and extract the vasculature. In particular, such approaches commonly search for specific characteristic features such as vasculature branching junctions. While such algorithms provide a reasonable degree of registration accuracy they are computationally inefficient, i.e. computationally expensive. Furthermore, such known algorithms only allow images obtained from common imaging modes to be registered. That is, such known algorithms do not allow images obtained from different imaging modes, such as reflectance or auto-fluorescence, to be registered.
Examples of such known algorithms can be found in the following publications: US 2012/0195481A; Can et al “A feature based, Robust, Hierarchical Algorithm for Registering Pairs of Images of the Curved Human Retina”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 24, No, 3 (March 2002); Zana & Klein, “A Multimodal Registration Algorithm of Eye Fundus Images Using Vessels Detection and Hough Transform”, IEEE Transactions on Medical Imaging, Vol 18, No 5 (May 1999); and Hu et al “Multimodal Retinal Vessel Segmentation From Spectral-Domain Optical Coherence Tomography and Fundus Photography”, IEEE Transactions on Medical Imaging, Vol 31, No 10 (October 2012).
EP 2 064 988 A (Kowa Company, Ltd.) proposes a device and method for creating retinal fundus “maps” by superimposing two or more fundus images on the basis of a matching probability score. Matching is performed on the basis of corner image data identified in a blood vessel extraction image. However, the inventors believe that the technique proposed in EP'988 will not find sufficient corner features in the vasculature in a typical retinal image to enable reliable matching and registration of images, especially between different imaging modes. Retinal images are subject to very variable lighting, and in high-resolution retinal images produced by modern SLOs, the vascular features are relatively smooth-sided features. Therefore corner extraction will not yield a great number of candidate points for matching, or else will be heavily influenced by noise of various types.